Local Memory & Knowledge

Keep both memory and knowledge on your own machine — a local LLM answers, a local Ollama embedder builds the vectors, and a local vector store holds them.

Quick Start

Pull the embedder

ollama pull nomic-embed-text

Local memory

from praisonaiagents import Agent

agent = Agent(
    name="assistant",
    instructions="Remember facts across sessions.",
    memory={
        "provider": "mongodb",
        "config": {
            "connection_string": "mongodb://localhost:27017/",
            "database": "praisonai",
            "use_vector_search": True,
            "embedder": {
                "provider": "ollama",
                "config": {"model": "nomic-embed-text"},
            },
        },
    },
)
agent.start("Remember that my favourite colour is teal")

Add local knowledge

from praisonaiagents import Knowledge

kb = Knowledge(config={
    "vector_store": {
        "provider": "mongodb",
        "config": {
            "connection_string": "mongodb://localhost:27017/",
            "database": "praisonai",
            "collection": "handbook",
        },
    },
    "embedder": {
        "provider": "ollama",
        "config": {"model": "nomic-embed-text"},
    },
})

kb.add("Refunds are processed within 14 days.")
kb.search("refund window")

Install the MongoDB extra first: pip install "praisonaiagents[mongodb]". Chroma is the default local vector store and needs no extra service.

How It Works

The same embedder block drives memory and knowledge — embed, store, and query all stay on your machine. The SDK now sizes the vector index at the model’s real dimension — nomic-embed-text is 768, not the 1536 default it was silently written as before PR #4802.

Choosing a Local Embedder

Pick by what matters most: speed, balance, or quality.

Model	Dimensions	Best for
`all-minilm`	384	Fastest, smallest footprint
`nomic-embed-text`	768	Balanced default
`mxbai-embed-large`	1024	Highest quality

Common Patterns

Local memory with a local LLM:

from praisonaiagents import Agent

agent = Agent(
    name="assistant",
    llm="ollama/llama3.2",
    memory={
        "provider": "mongodb",
        "config": {
            "connection_string": "mongodb://localhost:27017/",
            "database": "praisonai",
            "use_vector_search": True,
            "embedder": {"provider": "ollama", "config": {"model": "nomic-embed-text"}},
        },
    },
)
agent.start("Remember I prefer metric units")

Local knowledge with the Knowledge class:

from praisonaiagents import Knowledge

kb = Knowledge(config={
    "vector_store": {
        "provider": "mongodb",
        "config": {
            "connection_string": "mongodb://localhost:27017/",
            "database": "praisonai",
            "collection": "handbook",
        },
    },
    "embedder": {"provider": "ollama", "config": {"model": "nomic-embed-text"}},
})
kb.add("Onboarding takes three steps: sign in, verify email, join a team.")
kb.search("onboarding steps")

Both together — one local LLM for memory, one local embedder for knowledge:

from praisonaiagents import Agent, Knowledge

local_embedder = {"provider": "ollama", "config": {"model": "nomic-embed-text"}}

kb = Knowledge(config={
    "vector_store": {
        "provider": "mongodb",
        "config": {
            "connection_string": "mongodb://localhost:27017/",
            "database": "praisonai",
            "collection": "handbook",
        },
    },
    "embedder": local_embedder,
})
kb.add("Support hours are 9-5 UTC.")

agent = Agent(
    name="assistant",
    llm="ollama/llama3.2",
    memory={
        "provider": "mongodb",
        "config": {
            "connection_string": "mongodb://localhost:27017/",
            "database": "praisonai",
            "use_vector_search": True,
            "embedder": local_embedder,
        },
    },
)
agent.start("What did we agree on last session?")

Best Practices

Re-create the vector index when you change the embedder

Atlas vector indexes are built for a fixed dimension. Moving from text-embedding-3-small (1536) to nomic-embed-text (768) means dropping and re-creating vector_index at the new size — otherwise writes succeed but searches error or return nothing.

Pull the embedder before running

The first embed call fails cold if the model isn’t downloaded. Run ollama pull nomic-embed-text before starting the agent.

Set api_base when Ollama isn't on localhost

Ollama defaults to http://localhost:11434. Point elsewhere by setting api_base explicitly on the embedder call or OLLAMA_HOST in the environment.

Prefer the embedder block form

The {"provider": "...", "config": {"model": "..."}} block works identically across memory, knowledge, and the MongoDB adapters — reuse one dict everywhere.

MongoDB Memory

Configure the embedder on the MongoDB memory store

MongoDB Knowledge

Scoped, vector-searchable knowledge in MongoDB

Ollama Embeddings

Local embedding models and auto-detected dimensions

Local Models

Run the LLM side fully local

Getting Started

Install

Core Concepts

Guides

Features

Models

Databases

Observability

Memory

Telemetry & Analytics

Knowledge

RAG

Persistence

Tools

Other Features

Developers

Configuration

Best Practices

Getting Started (No Code)

Local Memory & Knowledge

Quick Start

How It Works

Choosing a Local Embedder

Common Patterns

Best Practices

MongoDB Memory

MongoDB Knowledge

Ollama Embeddings

Local Models

​Quick Start

​How It Works

​Choosing a Local Embedder

​Common Patterns

​Best Practices

​Related

MongoDB Memory

MongoDB Knowledge

Ollama Embeddings

Local Models

Quick Start

How It Works

Choosing a Local Embedder

Common Patterns

Best Practices

Related